What is happiness
Happiness is an emotional state characterized by feelings of joy,
satisfaction, contentment, and fulfillment. While happiness has many
different definitions, it is often described as involving positive
emotions and life satisfaction.
When most people talk about
happiness, they might be talking about how they feel in the present
moment, or they might be referring to a more general sense of how they
feel about life overall.
Because happiness tends to be such a
broadly defined term, psychologists and other social scientists
typically use the term ‘subjective well-being’ when they talk about this
emotional state. Just as it sounds, subjective well-being tends to focus
on an individual’s overall personal feelings about their life in the
present.
The aim of this project
The aim of this project is to observe the data from the World Happiness Report for several years and to exploratory data analysis to search for the difference in happiness levels between countries and try to postulate some hypothesis
About Dataset
Context
The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, the third in 2015, and the fourth in the 2016 Update. The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.
Content
The happiness scores and rankings use data from the Gallup World Poll. The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for the years 2013-2016 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others.
Inspiration
What countries or regions rank the highest in overall happiness and each of the six factors contributing to happiness? How did country ranks or scores change between the 2015 and 2016 as well as the 2016 and 2017 reports? Did any country experience a significant increase or decrease in happiness?
What is Dystopia?
Dystopia is an imaginary country that has the world’s least-happy people. The purpose in establishing Dystopia is to have a benchmark against which all countries can be favorably compared (no country performs more poorly than Dystopia) in terms of each of the six key variables, thus allowing each sub-bar to be of positive width. The lowest scores observed for the six key variables, therefore, characterize Dystopia. Since life would be very unpleasant in a country with the world’s lowest incomes, lowest life expectancy, lowest generosity, most corruption, least freedom and least social support, it is referred to as “Dystopia,” in contrast to Utopia.
What are the residuals?
The residuals, or unexplained components, differ for each country, reflecting the extent to which the six variables either over- or under-explain average 2014-2016 life evaluations. These residuals have an average value of approximately zero over the whole set of countries. Figure 2.2 shows the average residual for each country when the equation in Table 2.1 is applied to average 2014- 2016 data for the six variables in that country. We combine these residuals with the estimate for life evaluations in Dystopia so that the combined bar will always have positive values. As can be seen in Figure 2.2, although some life evaluation residuals are quite large, occasionally exceeding one point on the scale from 0 to 10, they are always much smaller than the calculated value in Dystopia, where the average life is rated at 1.85 on the 0 to 10 scale.
What do the columns succeeding the Happiness Score(like Family, Generosity, etc.) describe?
The following columns: GDP per Capita, Family, Life Expectancy, Freedom, Generosity, Trust Government Corruption describe the extent to which these factors contribute in evaluating the happiness in each country. The Dystopia Residual metric actually is the Dystopia Happiness Score(1.85) + the Residual value or the unexplained value for each country as stated in the previous answer.
If you add all these factors up, you get the happiness score so it might be un-reliable to model them to predict Happiness Scores.
d2015 <- read.csv("datasets/2015.csv")
d2016 <- read.csv("datasets/2016.csv")
d2017 <- read.csv("datasets/2017.csv")
d2018 <- read.csv("datasets/2018.csv")
d2019 <- read.csv("datasets/2019.csv")
head(d2015)
| Country | Region | Happiness.Rank | Happiness.Score | Standard.Error | Economy..GDP.per.Capita. | Family | Health..Life.Expectancy. | Freedom | Trust..Government.Corruption. | Generosity | Dystopia.Residual |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Switzerland | Western Europe | 1 | 7.587 | 0.03411 | 1.39651 | 1.34951 | 0.94143 | 0.66557 | 0.41978 | 0.29678 | 2.51738 |
| Iceland | Western Europe | 2 | 7.561 | 0.04884 | 1.30232 | 1.40223 | 0.94784 | 0.62877 | 0.14145 | 0.43630 | 2.70201 |
| Denmark | Western Europe | 3 | 7.527 | 0.03328 | 1.32548 | 1.36058 | 0.87464 | 0.64938 | 0.48357 | 0.34139 | 2.49204 |
| Norway | Western Europe | 4 | 7.522 | 0.03880 | 1.45900 | 1.33095 | 0.88521 | 0.66973 | 0.36503 | 0.34699 | 2.46531 |
| Canada | North America | 5 | 7.427 | 0.03553 | 1.32629 | 1.32261 | 0.90563 | 0.63297 | 0.32957 | 0.45811 | 2.45176 |
| Finland | Western Europe | 6 | 7.406 | 0.03140 | 1.29025 | 1.31826 | 0.88911 | 0.64169 | 0.41372 | 0.23351 | 2.61955 |
head(d2016)
| Country | Region | Happiness.Rank | Happiness.Score | Lower.Confidence.Interval | Upper.Confidence.Interval | Economy..GDP.per.Capita. | Family | Health..Life.Expectancy. | Freedom | Trust..Government.Corruption. | Generosity | Dystopia.Residual |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Denmark | Western Europe | 1 | 7.526 | 7.460 | 7.592 | 1.44178 | 1.16374 | 0.79504 | 0.57941 | 0.44453 | 0.36171 | 2.73939 |
| Switzerland | Western Europe | 2 | 7.509 | 7.428 | 7.590 | 1.52733 | 1.14524 | 0.86303 | 0.58557 | 0.41203 | 0.28083 | 2.69463 |
| Iceland | Western Europe | 3 | 7.501 | 7.333 | 7.669 | 1.42666 | 1.18326 | 0.86733 | 0.56624 | 0.14975 | 0.47678 | 2.83137 |
| Norway | Western Europe | 4 | 7.498 | 7.421 | 7.575 | 1.57744 | 1.12690 | 0.79579 | 0.59609 | 0.35776 | 0.37895 | 2.66465 |
| Finland | Western Europe | 5 | 7.413 | 7.351 | 7.475 | 1.40598 | 1.13464 | 0.81091 | 0.57104 | 0.41004 | 0.25492 | 2.82596 |
| Canada | North America | 6 | 7.404 | 7.335 | 7.473 | 1.44015 | 1.09610 | 0.82760 | 0.57370 | 0.31329 | 0.44834 | 2.70485 |
head(d2017)
| Country | Happiness.Rank | Happiness.Score | Whisker.high | Whisker.low | Economy..GDP.per.Capita. | Family | Health..Life.Expectancy. | Freedom | Generosity | Trust..Government.Corruption. | Dystopia.Residual |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Norway | 1 | 7.537 | 7.594445 | 7.479556 | 1.616463 | 1.533524 | 0.7966665 | 0.6354226 | 0.3620122 | 0.3159638 | 2.277027 |
| Denmark | 2 | 7.522 | 7.581728 | 7.462272 | 1.482383 | 1.551122 | 0.7925655 | 0.6260067 | 0.3552805 | 0.4007701 | 2.313707 |
| Iceland | 3 | 7.504 | 7.622031 | 7.385970 | 1.480633 | 1.610574 | 0.8335521 | 0.6271626 | 0.4755402 | 0.1535266 | 2.322715 |
| Switzerland | 4 | 7.494 | 7.561772 | 7.426228 | 1.564980 | 1.516912 | 0.8581313 | 0.6200706 | 0.2905493 | 0.3670073 | 2.276716 |
| Finland | 5 | 7.469 | 7.527542 | 7.410458 | 1.443572 | 1.540247 | 0.8091577 | 0.6179509 | 0.2454828 | 0.3826115 | 2.430182 |
| Netherlands | 6 | 7.377 | 7.427426 | 7.326574 | 1.503945 | 1.428939 | 0.8106961 | 0.5853845 | 0.4704898 | 0.2826618 | 2.294804 |
head(d2018)
| Overall.rank | Country.or.region | Score | GDP.per.capita | Social.support | Healthy.life.expectancy | Freedom.to.make.life.choices | Generosity | Perceptions.of.corruption |
|---|---|---|---|---|---|---|---|---|
| 1 | Finland | 7.632 | 1.305 | 1.592 | 0.874 | 0.681 | 0.202 | 0.393 |
| 2 | Norway | 7.594 | 1.456 | 1.582 | 0.861 | 0.686 | 0.286 | 0.340 |
| 3 | Denmark | 7.555 | 1.351 | 1.590 | 0.868 | 0.683 | 0.284 | 0.408 |
| 4 | Iceland | 7.495 | 1.343 | 1.644 | 0.914 | 0.677 | 0.353 | 0.138 |
| 5 | Switzerland | 7.487 | 1.420 | 1.549 | 0.927 | 0.660 | 0.256 | 0.357 |
| 6 | Netherlands | 7.441 | 1.361 | 1.488 | 0.878 | 0.638 | 0.333 | 0.295 |
head(d2019)
| Overall.rank | Country.or.region | Score | GDP.per.capita | Social.support | Healthy.life.expectancy | Freedom.to.make.life.choices | Generosity | Perceptions.of.corruption |
|---|---|---|---|---|---|---|---|---|
| 1 | Finland | 7.769 | 1.340 | 1.587 | 0.986 | 0.596 | 0.153 | 0.393 |
| 2 | Denmark | 7.600 | 1.383 | 1.573 | 0.996 | 0.592 | 0.252 | 0.410 |
| 3 | Norway | 7.554 | 1.488 | 1.582 | 1.028 | 0.603 | 0.271 | 0.341 |
| 4 | Iceland | 7.494 | 1.380 | 1.624 | 1.026 | 0.591 | 0.354 | 0.118 |
| 5 | Netherlands | 7.488 | 1.396 | 1.522 | 0.999 | 0.557 | 0.322 | 0.298 |
| 6 | Switzerland | 7.480 | 1.452 | 1.526 | 1.052 | 0.572 | 0.263 | 0.343 |
First 5 countries
First let’s see the top
5 for the years 2015, 2016. 2017, 2018, and 2019.
d2015[,c("Country", "Happiness.Rank", "Happiness.Score")] |> head(5)
## Country Happiness.Rank Happiness.Score
## 1 Switzerland 1 7.587
## 2 Iceland 2 7.561
## 3 Denmark 3 7.527
## 4 Norway 4 7.522
## 5 Canada 5 7.427
d2016[,c("Country", "Happiness.Rank", "Happiness.Score")] |> head(5)
## Country Happiness.Rank Happiness.Score
## 1 Denmark 1 7.526
## 2 Switzerland 2 7.509
## 3 Iceland 3 7.501
## 4 Norway 4 7.498
## 5 Finland 5 7.413
d2017[,c("Country", "Happiness.Rank", "Happiness.Score")] |> head(5)
## Country Happiness.Rank Happiness.Score
## 1 Norway 1 7.537
## 2 Denmark 2 7.522
## 3 Iceland 3 7.504
## 4 Switzerland 4 7.494
## 5 Finland 5 7.469
d2018[,c("Country.or.region", "Overall.rank", "Score")] |> head(5)
## Country.or.region Overall.rank Score
## 1 Finland 1 7.632
## 2 Norway 2 7.594
## 3 Denmark 3 7.555
## 4 Iceland 4 7.495
## 5 Switzerland 5 7.487
d2019[,c("Country.or.region", "Overall.rank", "Score")] |> head(5)
## Country.or.region Overall.rank Score
## 1 Finland 1 7.769
## 2 Denmark 2 7.600
## 3 Norway 3 7.554
## 4 Iceland 4 7.494
## 5 Netherlands 5 7.488
We can clearly see that the nordic countries like Finland, Norway and
Denkmare are constantly at the top.
Last 5
countries
Now let’s see at the 5 countries that score the
least.
d2015[,c("Country", "Happiness.Rank", "Happiness.Score")] |> tail(5)
## Country Happiness.Rank Happiness.Score
## 154 Rwanda 154 3.465
## 155 Benin 155 3.340
## 156 Syria 156 3.006
## 157 Burundi 157 2.905
## 158 Togo 158 2.839
d2016[,c("Country", "Happiness.Rank", "Happiness.Score")] |> tail(5)
## Country Happiness.Rank Happiness.Score
## 153 Benin 153 3.484
## 154 Afghanistan 154 3.360
## 155 Togo 155 3.303
## 156 Syria 156 3.069
## 157 Burundi 157 2.905
d2017[,c("Country", "Happiness.Rank", "Happiness.Score")] |> tail(5)
## Country Happiness.Rank Happiness.Score
## 151 Rwanda 151 3.471
## 152 Syria 152 3.462
## 153 Tanzania 153 3.349
## 154 Burundi 154 2.905
## 155 Central African Republic 155 2.693
d2018[,c("Country.or.region", "Overall.rank", "Score")] |> tail(5)
## Country.or.region Overall.rank Score
## 152 Yemen 152 3.355
## 153 Tanzania 153 3.303
## 154 South Sudan 154 3.254
## 155 Central African Republic 155 3.083
## 156 Burundi 156 2.905
d2019[,c("Country.or.region", "Overall.rank", "Score")] |> tail(5)
## Country.or.region Overall.rank Score
## 152 Rwanda 152 3.334
## 153 Tanzania 153 3.231
## 154 Afghanistan 154 3.203
## 155 Central African Republic 155 3.083
## 156 South Sudan 156 2.853
library('ggplot2')
Intresting correlations
Overall
Let’s see overall some correlations between some columns of the dataset and the score.
fit <- lm(Score ~ GDP.per.capita + Social.support + Healthy.life.expectancy + Freedom.to.make.life.choices + Generosity + Perceptions.of.corruption, data=d2019)
avPlots(fit, ask=FALSE)
Now let’s go into the specifics of some intresing correlation
GDP and happiness score
It’s intresting to see some correlations between the data in order to understand how there could be massive differences between the first and the last five countries . For example, the following is the correlation between the life expectancy and the happiness score, displaying also the life expectancy. We can see that the correlation is quite stable across time.
ggplot(d2015) + geom_point(aes(x=Happiness.Score, y=Economy..GDP.per.Capita. , col=Health..Life.Expectancy.))
ggplot(d2016) + geom_point(aes(x=Happiness.Score, y=Economy..GDP.per.Capita. , col=Health..Life.Expectancy.))
ggplot(d2017) + geom_point(aes(x=Happiness.Score, y=Economy..GDP.per.Capita. , col=Health..Life.Expectancy.))
ggplot(d2018) + geom_point(aes(x=Score, y=GDP.per.capita , col=Healthy.life.expectancy))
ggplot(d2019) + geom_point(aes(x=Score, y=GDP.per.capita , col=Healthy.life.expectancy))
Generosity and GDP
It’s intrenting to see if when people are richer they care less about the generosity of others. In order to answer this question it’s sufficient to display the correlation between Generosity and GDP.
g <- ggplot(d2019, aes(Generosity, GDP.per.capita))
g + geom_point() +
geom_smooth(method="lm", se=F)
## `geom_smooth()` using formula 'y ~ x'
It seems that people are more sensitive to generosity when they have less resources, which makes sense.
Perception of corruption and score
Now let’s see how much the perception of corruption influences the happiness score
g <- ggplot(d2019, aes(Perceptions.of.corruption, Score))
g + geom_point() +
geom_smooth(method="lm", se=F)
## `geom_smooth()` using formula 'y ~ x'
Freedom to make life choices and score
Now let’s see how much the perception of corruption influences the happiness score
g <- ggplot(d2019, aes(Freedom.to.make.life.choices, Score))
g + geom_point() +
geom_smooth(method="lm", se=F)
## `geom_smooth()` using formula 'y ~ x'
It doesn’t surprise that the more freedom to make life choices, the more people are happy.
Now let’s see the average happiness score by Region (year 2015)
r <- d2015 %>%
select(Region, Happiness.Score) %>%
group_by(Region) %>%
summarise(n = n(),
happinessMean = mean(Happiness.Score),
happinessMedian = median(Happiness.Score),
happinessStandard = sd(Happiness.Score))
ggplot(r, aes(x = fct_reorder(Region, happinessMedian), y = happinessMean, fill = happinessMean)) +
geom_bar(stat = "Identity", show.legend = F) +
scale_fill_gradient(low = "red", high = "green") +
labs(y="", x="",fill = "Score",
title = "Average Happiness score grouped by Region") +
theme_light(base_size = 9) + coord_flip()
The result shows that the region where people feel more happy is “Australia and New Zealand”
Now let’s see the average GDP per capita score by Region (year 2015)
r <- d2015 %>%
select(Region, Economy..GDP.per.Capita.) %>%
group_by(Region) %>%
summarise(n = n(),
gdpMean = mean(Economy..GDP.per.Capita.),
gdpMedian = median(Economy..GDP.per.Capita.),
happinessStandard = sd(Economy..GDP.per.Capita.))
ggplot(r, aes(x = fct_reorder(Region, gdpMedian), y = gdpMean, fill = gdpMean)) +
geom_bar(stat = "Identity", show.legend = F) +
scale_fill_gradient(low = "black", high = "blue") +
labs(y="", x="",fill = "Economy..GDP.per.Capita.",
title = "Average GDP score grouped by Region") +
theme_light(base_size = 9) + coord_flip()
Despite Australia and New Zealand is at the top for happiness score it is not for the GDP score per capita. As we can see, the first for GDP is North America, followed by Western Europe.
Let’s have a look at the distribution of density of the happiness score
ggplot(d2019,aes(Score))+
geom_density()
median(d2019$Score)
## [1] 5.3795
mean(d2019$Score)
## [1] 5.407096
sd(d2019$Score)
## [1] 1.11312
As most complex phenomena, the distribution is a Gaussian curve. The unusual thing is that it is quite flat at the mean. Furthermore we can see that both the mean and the median are above the theoretical mean (that would be (10-0)/2=5). This is an optimistic data.
Now let’s see if between 2015 an 2019 the mean and the median have shifted
mean(d2015$Happiness.Score)
## [1] 5.375734
mean(d2016$Happiness.Score)
## [1] 5.382185
mean(d2017$Happiness.Score)
## [1] 5.354019
mean(d2018$Score)
## [1] 5.375917
mean(d2019$Score)
## [1] 5.407096
mean(d2019$Score)-mean(d2015$Happiness.Score)
## [1] 0.03136198
While the technical answer is never, we can see that from 2015 and and 2019 there is been an overall improvement of 0.03136198 in the level of happiness of people. This theoretically and utopically would mean that, assuming a costant rate of improvement in (10-mean(d2019\(Score))/(mean(d2019\)Score)-mean(d2015$Happiness.Score))*(2019-2015)=585 years years the mean would reach 10.0
We saw the clear correlation between happiness and the GDP per capita. One likely way to improve the happiness of most people would be to improve the average level of wealth. Of course, while doing so, there are other things to improve such as removing the corruption and enhancing freedom.
(10-mean(d2019$Score))/(mean(d2019$Score)-mean(d2015$Happiness.Score))*(2019-2015)
## [1] 585.7926
Bibliography
* what is
happiness
* Dataset